Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (6): 409-415.doi: 10.11896/jsjkx.230400003

• Information Security • Previous Articles     Next Articles

Browser Fingerprint Tracking Based on Improved GraphSAGE Algorithm

CHU Xiaoxi1, ZHANG Jianhui2, ZHANG Desheng1, SU Hui1   

  1. 1 School of Cyber Science and Engineering,Zhengzhou University,Zhengzhou 450000,China
    2 Songshan Laboratory,Zhengzhou 450000,China
  • Received:2023-04-03 Revised:2023-08-02 Online:2024-06-15 Published:2024-06-05
  • About author:CHU Xiaoxi,born in 1999,postgra-duate.Her main research interests include cyberspace security and so on.
    ZHANG Jianhui,born in 1977,Ph.D,associate researcher,master supervi-sor.His main research interests include new network architecture,network routing technology,network data analysis and security control.
  • Supported by:
    National Key Research and Development Program of China(2022YFB2901403) and Major Science and TechnologyProgram of Henan Province(221100210900-01).

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Abstract: The current Web tracking field mainly uses browser fingerprint to track users,and for the problems of browser fingerprint tracking technology such as dynamic changes of fingerprint over time and the difficulty of long-term tracking,an improved graph sampling aggregation algorithm NE-GraphSAGE is proposed for browser fingerprint tracking. Firstly,the graph data is constructed using browser fingerprint as nodes and feature similarity between fingerprints as edges. Secondly,the GraphSAGE algorithm in graph neural networks is improved to not only focus on node features,but also capture edge information and classify edges to identify fingerprint. Finally,the NE-GraphSAGE algorithm is compared with Eckersley algorithm,FPStarker algorithm,and LSTM algorithm to verify the recognition effect of NE-GraphSAGE algorithm. Experimental results show that the NE-GraphSAGE algorithm has different degrees of improvement in accuracy and tracking time,and the maximum tracking time is up to 80 days. Compared with the other three algorithms,the NE-GraphSAGE algorithm has better performance,verifying its ability to track browser fingerprint for a long time.

Key words: Browser fingerprint, Graph neural network, GraphSAGE algorithm, User racking, Edge classification

CLC Number: 

  • TP393
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